






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_fnet import FNetTokenizer
else:
							__UpperCamelCase       :		Optional[int]							=    None

__UpperCamelCase       :		int							=    logging.get_logger(__name__)
__UpperCamelCase       :		List[Any]							=    {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__UpperCamelCase       :		Dict							=    {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

__UpperCamelCase       :		int							=    {
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

__UpperCamelCase       :		Union[str, Any]							=    """▁"""


class 	__SCREAMING_SNAKE_CASE(						_lowerCAmelCase						):
							_UpperCAmelCase			     =							VOCAB_FILES_NAMES
							_UpperCAmelCase			     =							PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase			     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase			     =							['''input_ids''', '''token_type_ids''']
							_UpperCAmelCase			     =							FNetTokenizer
							def __init__(  self:   int							,						UpperCamelCase:   str=None							,						UpperCamelCase:   Optional[Any]=None							,						UpperCamelCase:   List[Any]=False							,						UpperCamelCase:   Any=True							,						UpperCamelCase:   Optional[int]=True							,						UpperCamelCase:   int="<unk>"							,						UpperCamelCase:   List[str]="[SEP]"							,						UpperCamelCase:   int="<pad>"							,						UpperCamelCase:   Optional[Any]="[CLS]"							,						UpperCamelCase:   int="[MASK]"							,						**UpperCamelCase:   Optional[Any]							,						)		->		str:
											snake_case__						=  (
											    AddedToken(UpperCAmelCase_							,						lstrip=UpperCAmelCase_							,						rstrip=UpperCAmelCase_							,						normalized=UpperCAmelCase_    )
											    if isinstance(UpperCAmelCase_							,						UpperCAmelCase_    )
											    else mask_token
											)

											super().__init__(
											    UpperCAmelCase_							,						tokenizer_file=UpperCAmelCase_							,						do_lower_case=UpperCAmelCase_							,						remove_space=UpperCAmelCase_							,						keep_accents=UpperCAmelCase_							,						unk_token=UpperCAmelCase_							,						sep_token=UpperCAmelCase_							,						pad_token=UpperCAmelCase_							,						cls_token=UpperCAmelCase_							,						mask_token=UpperCAmelCase_							,						**UpperCAmelCase_							,						)

											snake_case__						=  do_lower_case
											snake_case__						=  remove_space
											snake_case__						=  keep_accents
											snake_case__						=  vocab_file
											snake_case__						=  False if not self.vocab_file else True
							def lowerCAmelCase_			(  self:   str							,						UpperCamelCase:   List[int]							,						UpperCamelCase:   Optional[List[int]] = None    )		->		List[int]:
											snake_case__						=  [self.sep_token_id]
											snake_case__						=  [self.cls_token_id]
											if token_ids_a is None:
															return cls + token_ids_a + sep
											return cls + token_ids_a + sep + token_ids_a + sep
							def lowerCAmelCase_			(  self:   int							,						UpperCamelCase:   List[int]							,						UpperCamelCase:   Optional[List[int]] = None    )		->		List[int]:
											snake_case__						=  [self.sep_token_id]
											snake_case__						=  [self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep    ) * [0]
											return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]


							def lowerCAmelCase_			(  self:   Union[str, Any]							,						UpperCamelCase:   str							,						UpperCamelCase:   Optional[str] = None    )		->		Tuple[str]:
											if not os.path.isdir(UpperCAmelCase_    ):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
															return
											snake_case__						=  os.path.join(
											    UpperCAmelCase_							,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

											if os.path.abspath(self.vocab_file    ) != os.path.abspath(UpperCAmelCase_    ):
															copyfile(self.vocab_file							,						UpperCAmelCase_    )

											return (out_vocab_file,)






"""simple docstring"""


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  {
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       Any				=      '''deberta-v2'''






       def __init__(	self			:				Dict							,   UpperCAmelCase_			:				Optional[int]=128_100							,   UpperCAmelCase_			:				List[str]=1_536							,   UpperCAmelCase_			:				List[str]=24							,   UpperCAmelCase_			:				int=24							,   UpperCAmelCase_			:				int=6_144							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   UpperCAmelCase_			:				Tuple=0.1							,   UpperCAmelCase_			:				int=0.1							,   UpperCAmelCase_			:				Optional[int]=512							,   UpperCAmelCase_			:				Optional[int]=0							,   UpperCAmelCase_			:				Dict=0.02							,   UpperCAmelCase_			:				Optional[Any]=1e-7							,   UpperCAmelCase_			:				str=False							,   UpperCAmelCase_			:				Tuple=-1							,   UpperCAmelCase_			:				Optional[int]=0							,   UpperCAmelCase_			:				List[str]=True							,   UpperCAmelCase_			:				Any=None							,   UpperCAmelCase_			:				Optional[Any]=0							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   **UpperCAmelCase_			:				Optional[int]							,   )->	int:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_      )

              UpperCamelCase       			=		hidden_size
              UpperCamelCase       			=		num_hidden_layers
              UpperCamelCase       			=		num_attention_heads
              UpperCamelCase       			=		intermediate_size
              UpperCamelCase       			=		hidden_act
              UpperCamelCase       			=		hidden_dropout_prob
              UpperCamelCase       			=		attention_probs_dropout_prob
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		type_vocab_size
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		relative_attention
              UpperCamelCase       			=		max_relative_positions
              UpperCamelCase       			=		pad_token_id
              UpperCamelCase       			=		position_biased_input

              # Backwards compatibility
              if type(UpperCAmelCase_      ) == str:
                     UpperCamelCase       			=		[x.strip() for x in pos_att_type.lower().split("|"      )]

              UpperCamelCase       			=		pos_att_type
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		layer_norm_eps

              UpperCamelCase       			=		kwargs.get("pooler_hidden_size"							,   UpperCAmelCase_      )
              UpperCamelCase       			=		pooler_dropout
              UpperCamelCase       			=		pooler_hidden_act





class 	__a	(   _lowerCAmelCase						):






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Mapping[str, Mapping[int, str]]:







              """simple docstring"""
              if self.task == "multiple-choice":
                     UpperCamelCase       			=		{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     UpperCamelCase       			=		{0: "batch", 1: "sequence"}
              if self._config.type_vocab_size > 0:
                     return OrderedDict(
                         [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]      )
              else:
                     return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	int:







              """simple docstring"""
              return 12






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional["TensorType"] = None							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				int = 40							,   UpperCAmelCase_			:				int = 40							,   UpperCAmelCase_			:				"PreTrainedTokenizerBase" = None							,   )->	Mapping[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		super().generate_dummy_inputs(preprocessor=UpperCAmelCase_							,   framework=UpperCAmelCase_      )
              if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                     del dummy_inputs["token_type_ids"]
              return dummy_inputs





